#  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at:
#
#       https://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
#  or implied. See the License for the specific language governing
#  permissions and limitations under the License.


from typing import Tuple

import numpy as np
from typing_extensions import Annotated

from zenml import step


@step
def normalizer(
    X_train: np.ndarray, X_test: np.ndarray
) -> Tuple[
    Annotated[np.ndarray, "X_train_normed"],
    Annotated[np.ndarray, "X_test_normed"],
]:
    """Normalize the values for all the images so they are between 0 and 1."""
    x_train_normed = X_train / 255.0
    x_test_normed = X_test / 255.0
    return x_train_normed, x_test_normed
